Glow in the Dark: Smartphone Inertial Odometry for Vehicle Tracking in GPS Blocked Environments

Although vehicle location-based services are prevalent outdoors, we are back into darkness in many GPS blocked environments, such as tunnels, indoor parking garages, and multilevel flyovers. Existing smartphone-based solutions usually adopt inertial dead reckoning to infer the trajectory, but low-qu...

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Bibliographic Details
Published in:IEEE internet of things journal 2021-08, Vol.8 (16), p.12955-12967
Main Authors: Gao, Ruipeng, Xiao, Xuan, Zhu, Shuli, Xing, Weiwei, Li, Chi, Liu, Lei, Ma, Li, Chai, Hua
Format: Article
Language:English
Subjects:
Acceleration
Automobiles
Car sharing
Darkness
Dead reckoning
Federated learning
Global Positioning System
Gyroscopes
Indoor environments
Inertial sensing devices
inertial sequence learning
Location based services
Odometers
Smartphones
Traffic information
Training
Trajectory
Transforms
vehicle tracking
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Summary:Although vehicle location-based services are prevalent outdoors, we are back into darkness in many GPS blocked environments, such as tunnels, indoor parking garages, and multilevel flyovers. Existing smartphone-based solutions usually adopt inertial dead reckoning to infer the trajectory, but low-quality inertial sensors in phones are plagued by heavy noises, causing unbounded localization errors through double integrations for movements. In this article, we propose VeTorch , a smartphone inertial odometry that devises an inertial sequence learning framework to track vehicles in real time when GPS signal is not available. Specifically, we transform the inertial dynamics from the phone to the vehicle regardless of the arbitrary phone's placement in the car and explore a temporal convolutional network to learn the vehicle's moving dependencies directly from the inertial data. To tackle the heterogeneous smartphone properties and driving habits, we propose a federated learning-based active model training mechanism to produce customized models for individual smartphones, without incurring user privacy issues. We implement a highly efficient prototype and conduct extensive experiments on two large-scale real-world traffic data sets collected by a modern ride-hailing platform. Our results outperform the state-of-the-art vehicular inertial dead-reckoning solutions on both accuracy and efficiency.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2021.3064342